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Manufacturing

IoT-Connected Smart Factory with Predictive Quality Management

Manufacturing

4.8% → 1.2%
Scrap Rate
14% → 4%
Machine Downtime
+22%
OEE Improvement
88% → 96%
Forecast Accuracy
The Challenge

What They Were Facing

Apex operated four production facilities with 120 CNC machines and injection moulding lines producing aerospace-grade components. Quality defects were detected only at end-of-line inspection, resulting in a 4.8% scrap rate and $3.2M in annual rework costs. Machine downtime averaged 14% due to reactive maintenance, and production supervisors relied on paper-based logs with no real-time visibility into line performance or OEE metrics.

Challenge context
The Solution

How We Solved It

AQBEE deployed an IoT-connected smart factory platform integrating Salesforce Manufacturing Cloud with AWS IoT Core for real-time machine telemetry. Over 600 sensors were connected across all four facilities, streaming vibration, temperature, pressure, and cycle-time data into AWS Timestream. Machine learning models built on Amazon SageMaker predicted quality deviations before defective parts were produced, triggering automated parameter adjustments and operator alerts. Salesforce Manufacturing Cloud provided account-based forecasting and sales agreement tracking, while MuleSoft connected ERP, MES, and supply chain systems. Tableau dashboards displayed real-time OEE, scrap rates, and predictive maintenance schedules for every production line.

Technology Stack
Salesforce Manufacturing CloudAWS IoT CoreAmazon SageMakerAWS TimestreamMuleSoftTableauFlow Automation
Measurable Results

The Impact

4.8% → 1.2%
Scrap Rate

Predictive quality models detected deviations before defective parts were produced, reducing the scrap rate from 4.8% to 1.2% — saving $2.6M annually.

14% → 4%
Machine Downtime

Predictive maintenance models analysed vibration and temperature patterns to schedule maintenance before failures, cutting unplanned downtime from 14% to 4%.

+22%
OEE Improvement

Real-time OEE visibility and automated alerting drove a 22% overall equipment effectiveness improvement across all four facilities.

88% → 96%
Forecast Accuracy

Manufacturing Cloud's account-based forecasting improved demand forecast accuracy from 88% to 96%, reducing both overproduction and stockouts.

We used to find defects at the end of the line and throw away parts worth thousands of dollars. Now the system predicts a quality drift 20 minutes before it happens and adjusts the machine automatically. Our scrap rate dropped by 75%, and we have not had an unplanned line stoppage in three months. AQBEE turned our factories into something out of the future.

KE
Karl Engstrom
VP of Manufacturing

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